Neuromorphic processing has advanced sufficiently to enable its cost-effective use in performing routine tasks that were once solely addressed by non-neuromorphic instruction-based processing based on the execution of a series of instructions, rather than through use of a neural network. In various areas in which some minimal degree of inaccuracy in the results of processing large amounts of data is deemed acceptable, the use of neural networks of sufficient size and complexity to perform a function may enable one or more orders of magnitude in improvement of processing speed.
Distributed development of task routines and the performance of analysis tasks using pooled task routines with pooled data has advanced to an extent that the addition of mechanisms for organization of development and to provide oversight for reproducibility and accountability have become increasingly desired. In various scientific, technical and other areas, the quantities of data employed in performing analysis tasks have become ever larger, thereby making desirable the pooling of data objects to enable collaboration, share costs and/or improve access. Also, such large quantities of data, by virtue of the amount and detail of the information they contain, have become of such value that it has become desirable to find as many uses as possible for such data in peer reviewing and in as wide a variety of analysis tasks. Thus, the pooling of components of analysis routines to enable reuse, oversight and error checking has also become desirable.